Learning Time Series Models for Pedestrian Motion Prediction

Robot systems deployed in real-world environments often need to interact with other dynamic objects, such as pedestrians, cars, bicycles or other vehicles. In such cases, it is useful to have a good predictive model of the object's motion to factor in when optimizing the robot's own behaviour. In this paper we consider motion models cast in the Predictive Linear Gaussian (PLG) model, and propose two learning approaches for this framework: one based on the method of moments and the other on a least-squares criteria. We evaluate the approaches on several synthetic datasets, and deploy the system on a wheelchair robot, to improve its ability to follow a walking companion.